Early methods [1]}, [2]} were based on extensions of the sliding window scheme, requiring strong assumptions such as a cuboid shape, i.e., a fixed spatial extent of the actor across frames. Later, advancements in object detection inspired frame-level action detection methods to recognize human action classes at the frame level. In the first stage action proposals are produced by a region proposal algorithm or densely sampled anchors, and in the second stage the proposals are used for action classification and localization refinement. Hundreds of action proposals are extracted per video given low-level cues, such as super-voxels [3]}, [4]} or dense trajectories [5]}, [6]}, [7]}, and then proposals are classified to localize actions.
